Matrix Theorem (Kirkhoff) Clarification












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I know that if you take the determinant of the Laplacian Matrix, then you can get the number of spanning trees. However is that also the case for when you have either the Adjacency Matrix or the Degree Matrix?



If not, what would you have to do to find the amount of spanning trees if you were only provided the adjacency matrix (or degree matrix)?










share|cite|improve this question











$endgroup$

















    1












    $begingroup$


    I know that if you take the determinant of the Laplacian Matrix, then you can get the number of spanning trees. However is that also the case for when you have either the Adjacency Matrix or the Degree Matrix?



    If not, what would you have to do to find the amount of spanning trees if you were only provided the adjacency matrix (or degree matrix)?










    share|cite|improve this question











    $endgroup$















      1












      1








      1


      2



      $begingroup$


      I know that if you take the determinant of the Laplacian Matrix, then you can get the number of spanning trees. However is that also the case for when you have either the Adjacency Matrix or the Degree Matrix?



      If not, what would you have to do to find the amount of spanning trees if you were only provided the adjacency matrix (or degree matrix)?










      share|cite|improve this question











      $endgroup$




      I know that if you take the determinant of the Laplacian Matrix, then you can get the number of spanning trees. However is that also the case for when you have either the Adjacency Matrix or the Degree Matrix?



      If not, what would you have to do to find the amount of spanning trees if you were only provided the adjacency matrix (or degree matrix)?







      graph-theory






      share|cite|improve this question















      share|cite|improve this question













      share|cite|improve this question




      share|cite|improve this question








      edited Dec 7 '18 at 5:37









      Trevor Gunn

      14.3k32046




      14.3k32046










      asked Dec 7 '18 at 4:47









      DevAllanPerDevAllanPer

      1336




      1336






















          1 Answer
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          active

          oldest

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          1












          $begingroup$

          It does not work with the adjancency matrix nor the degree matrix. To see this, let's look at $K_4$, which we know has $4^{4 - 2}$ spanning trees. (There's nothing special about $K_4$, it's just the first graph I picked.)



          $K_4$ has $4$ vertices each of degree $3$. Applying Kirchoff rule to the degree matrix gives $3^3$ spanning trees (incorrect). Applying it to the adjacency matrix gives
          $$ left| begin{array}{ccc} 0 & 1 & 1 \ 1 & 0 & 1 \ 1 & 1 & 0 end{array} right| = 2$$
          (also incorrect).



          To understand why you need the Laplacian, it is helpful to understand a proof of Kirchoff's theorem. Here is one outline.



          Theorem 1. (Binet-Cauchy) If $A$ is $m times n$ and $B$ is $n times m$ then
          $$ det(AB) = sum_S det(A[S]) det(B[S]) $$
          where the sum is over all $m$-element sets of columns of $A$ and rows of $B$. Here $A[S]$ means we keep only the columns from $S$ and $B[S]$ means we keep only the rows from $S$.



          Lemma 2. Let $G$ be a connected graph with no loops (but multiple edges are allowed). Arbitrarily orient $G$ and let $D$ be the signed incidence matrix:



          $$ D_{ve} = begin{cases} 1 & v text{ is the head of } e \
          -1 & v text{ is the tail of } e \
          0 & text{otherwise} end{cases} $$



          (Rows are indexed by vertices and columns by edges.) Then $DD^T = L$, the Laplacian of $G$.



          Lemma 3. Let $G$ be a connected graph with no loops, oriented arbitrarily. Let $D$ be the signed incidence matrix. If $v in V(G)$ and $S subseteq E(G)$ then $D(v,S)$ denotes the matrix with row $v$ deleted and keeping only the columns belonging to $S$. (So our sets $S$ have $|V(G)| - 1$ elements.)



          Then $det D(v,S) = pm 1$ if $(V,S)$ is a spanning tree and $det(v,S) = 0$ if $(V,S)$ is not (i.e. it contains a cycle).





          There are of course other proofs of Kirchoff's Theorem. What we can see here is that Lemma 3 is the tool that connects spanning trees to determinants and it is a result about the signed incidence matrix. From Lemma 2 and Binet-Cauchy, we remove the issue with having a sign of $pm 1$ by squaring and we sum over all sets $S$ to count the total number of trees.



          Now, if you are given the adjacency matrix, it is easy enough to construct the degrees of each vertex and hence construct the number of spanning trees. On the other hand, if you only have the degrees, you don't know enough about the graph structure to count spanning trees. As an exercise, find two graphs with the same degree sequence but different numbers of spanning trees (remember you can use multiple edges).






          share|cite|improve this answer









          $endgroup$













          • $begingroup$
            First of all, thank you for your wonderful explanation! One question though, Are you suggesting that if I were given a large adjacency matrix, I should go ahead and try to map it out and then try to get the Laplacian matrix from that to get the spanning tree #?
            $endgroup$
            – DevAllanPer
            Dec 7 '18 at 5:46










          • $begingroup$
            Just for further reference, and so you can see what I mean, the matrix I was provided is: ibb.co/XXHL529 (i believe the missing # is 1)
            $endgroup$
            – DevAllanPer
            Dec 7 '18 at 5:48










          • $begingroup$
            @DevAllanPer By "map it out" do you mean draw it? That's unnecessary. All you need are the degrees and the adjacency matrix and you can read off the degrees from the adjacency matrix. For example, if vertex 1 is adjacent to vertices 5,7,10 then vertex 1 has degree 3.
            $endgroup$
            – Trevor Gunn
            Dec 7 '18 at 6:05










          • $begingroup$
            Yeah, but in my example (the link i provided) that creates a 8x8 matrix. I don't think our professor would be cruel enough to make us find the determinant of an 8x8 matrix, so whats the trick there?
            $endgroup$
            – DevAllanPer
            Dec 7 '18 at 15:59










          • $begingroup$
            @DevAllanPer Use a computer? I don't know.
            $endgroup$
            – Trevor Gunn
            Dec 7 '18 at 18:30











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          $begingroup$

          It does not work with the adjancency matrix nor the degree matrix. To see this, let's look at $K_4$, which we know has $4^{4 - 2}$ spanning trees. (There's nothing special about $K_4$, it's just the first graph I picked.)



          $K_4$ has $4$ vertices each of degree $3$. Applying Kirchoff rule to the degree matrix gives $3^3$ spanning trees (incorrect). Applying it to the adjacency matrix gives
          $$ left| begin{array}{ccc} 0 & 1 & 1 \ 1 & 0 & 1 \ 1 & 1 & 0 end{array} right| = 2$$
          (also incorrect).



          To understand why you need the Laplacian, it is helpful to understand a proof of Kirchoff's theorem. Here is one outline.



          Theorem 1. (Binet-Cauchy) If $A$ is $m times n$ and $B$ is $n times m$ then
          $$ det(AB) = sum_S det(A[S]) det(B[S]) $$
          where the sum is over all $m$-element sets of columns of $A$ and rows of $B$. Here $A[S]$ means we keep only the columns from $S$ and $B[S]$ means we keep only the rows from $S$.



          Lemma 2. Let $G$ be a connected graph with no loops (but multiple edges are allowed). Arbitrarily orient $G$ and let $D$ be the signed incidence matrix:



          $$ D_{ve} = begin{cases} 1 & v text{ is the head of } e \
          -1 & v text{ is the tail of } e \
          0 & text{otherwise} end{cases} $$



          (Rows are indexed by vertices and columns by edges.) Then $DD^T = L$, the Laplacian of $G$.



          Lemma 3. Let $G$ be a connected graph with no loops, oriented arbitrarily. Let $D$ be the signed incidence matrix. If $v in V(G)$ and $S subseteq E(G)$ then $D(v,S)$ denotes the matrix with row $v$ deleted and keeping only the columns belonging to $S$. (So our sets $S$ have $|V(G)| - 1$ elements.)



          Then $det D(v,S) = pm 1$ if $(V,S)$ is a spanning tree and $det(v,S) = 0$ if $(V,S)$ is not (i.e. it contains a cycle).





          There are of course other proofs of Kirchoff's Theorem. What we can see here is that Lemma 3 is the tool that connects spanning trees to determinants and it is a result about the signed incidence matrix. From Lemma 2 and Binet-Cauchy, we remove the issue with having a sign of $pm 1$ by squaring and we sum over all sets $S$ to count the total number of trees.



          Now, if you are given the adjacency matrix, it is easy enough to construct the degrees of each vertex and hence construct the number of spanning trees. On the other hand, if you only have the degrees, you don't know enough about the graph structure to count spanning trees. As an exercise, find two graphs with the same degree sequence but different numbers of spanning trees (remember you can use multiple edges).






          share|cite|improve this answer









          $endgroup$













          • $begingroup$
            First of all, thank you for your wonderful explanation! One question though, Are you suggesting that if I were given a large adjacency matrix, I should go ahead and try to map it out and then try to get the Laplacian matrix from that to get the spanning tree #?
            $endgroup$
            – DevAllanPer
            Dec 7 '18 at 5:46










          • $begingroup$
            Just for further reference, and so you can see what I mean, the matrix I was provided is: ibb.co/XXHL529 (i believe the missing # is 1)
            $endgroup$
            – DevAllanPer
            Dec 7 '18 at 5:48










          • $begingroup$
            @DevAllanPer By "map it out" do you mean draw it? That's unnecessary. All you need are the degrees and the adjacency matrix and you can read off the degrees from the adjacency matrix. For example, if vertex 1 is adjacent to vertices 5,7,10 then vertex 1 has degree 3.
            $endgroup$
            – Trevor Gunn
            Dec 7 '18 at 6:05










          • $begingroup$
            Yeah, but in my example (the link i provided) that creates a 8x8 matrix. I don't think our professor would be cruel enough to make us find the determinant of an 8x8 matrix, so whats the trick there?
            $endgroup$
            – DevAllanPer
            Dec 7 '18 at 15:59










          • $begingroup$
            @DevAllanPer Use a computer? I don't know.
            $endgroup$
            – Trevor Gunn
            Dec 7 '18 at 18:30
















          1












          $begingroup$

          It does not work with the adjancency matrix nor the degree matrix. To see this, let's look at $K_4$, which we know has $4^{4 - 2}$ spanning trees. (There's nothing special about $K_4$, it's just the first graph I picked.)



          $K_4$ has $4$ vertices each of degree $3$. Applying Kirchoff rule to the degree matrix gives $3^3$ spanning trees (incorrect). Applying it to the adjacency matrix gives
          $$ left| begin{array}{ccc} 0 & 1 & 1 \ 1 & 0 & 1 \ 1 & 1 & 0 end{array} right| = 2$$
          (also incorrect).



          To understand why you need the Laplacian, it is helpful to understand a proof of Kirchoff's theorem. Here is one outline.



          Theorem 1. (Binet-Cauchy) If $A$ is $m times n$ and $B$ is $n times m$ then
          $$ det(AB) = sum_S det(A[S]) det(B[S]) $$
          where the sum is over all $m$-element sets of columns of $A$ and rows of $B$. Here $A[S]$ means we keep only the columns from $S$ and $B[S]$ means we keep only the rows from $S$.



          Lemma 2. Let $G$ be a connected graph with no loops (but multiple edges are allowed). Arbitrarily orient $G$ and let $D$ be the signed incidence matrix:



          $$ D_{ve} = begin{cases} 1 & v text{ is the head of } e \
          -1 & v text{ is the tail of } e \
          0 & text{otherwise} end{cases} $$



          (Rows are indexed by vertices and columns by edges.) Then $DD^T = L$, the Laplacian of $G$.



          Lemma 3. Let $G$ be a connected graph with no loops, oriented arbitrarily. Let $D$ be the signed incidence matrix. If $v in V(G)$ and $S subseteq E(G)$ then $D(v,S)$ denotes the matrix with row $v$ deleted and keeping only the columns belonging to $S$. (So our sets $S$ have $|V(G)| - 1$ elements.)



          Then $det D(v,S) = pm 1$ if $(V,S)$ is a spanning tree and $det(v,S) = 0$ if $(V,S)$ is not (i.e. it contains a cycle).





          There are of course other proofs of Kirchoff's Theorem. What we can see here is that Lemma 3 is the tool that connects spanning trees to determinants and it is a result about the signed incidence matrix. From Lemma 2 and Binet-Cauchy, we remove the issue with having a sign of $pm 1$ by squaring and we sum over all sets $S$ to count the total number of trees.



          Now, if you are given the adjacency matrix, it is easy enough to construct the degrees of each vertex and hence construct the number of spanning trees. On the other hand, if you only have the degrees, you don't know enough about the graph structure to count spanning trees. As an exercise, find two graphs with the same degree sequence but different numbers of spanning trees (remember you can use multiple edges).






          share|cite|improve this answer









          $endgroup$













          • $begingroup$
            First of all, thank you for your wonderful explanation! One question though, Are you suggesting that if I were given a large adjacency matrix, I should go ahead and try to map it out and then try to get the Laplacian matrix from that to get the spanning tree #?
            $endgroup$
            – DevAllanPer
            Dec 7 '18 at 5:46










          • $begingroup$
            Just for further reference, and so you can see what I mean, the matrix I was provided is: ibb.co/XXHL529 (i believe the missing # is 1)
            $endgroup$
            – DevAllanPer
            Dec 7 '18 at 5:48










          • $begingroup$
            @DevAllanPer By "map it out" do you mean draw it? That's unnecessary. All you need are the degrees and the adjacency matrix and you can read off the degrees from the adjacency matrix. For example, if vertex 1 is adjacent to vertices 5,7,10 then vertex 1 has degree 3.
            $endgroup$
            – Trevor Gunn
            Dec 7 '18 at 6:05










          • $begingroup$
            Yeah, but in my example (the link i provided) that creates a 8x8 matrix. I don't think our professor would be cruel enough to make us find the determinant of an 8x8 matrix, so whats the trick there?
            $endgroup$
            – DevAllanPer
            Dec 7 '18 at 15:59










          • $begingroup$
            @DevAllanPer Use a computer? I don't know.
            $endgroup$
            – Trevor Gunn
            Dec 7 '18 at 18:30














          1












          1








          1





          $begingroup$

          It does not work with the adjancency matrix nor the degree matrix. To see this, let's look at $K_4$, which we know has $4^{4 - 2}$ spanning trees. (There's nothing special about $K_4$, it's just the first graph I picked.)



          $K_4$ has $4$ vertices each of degree $3$. Applying Kirchoff rule to the degree matrix gives $3^3$ spanning trees (incorrect). Applying it to the adjacency matrix gives
          $$ left| begin{array}{ccc} 0 & 1 & 1 \ 1 & 0 & 1 \ 1 & 1 & 0 end{array} right| = 2$$
          (also incorrect).



          To understand why you need the Laplacian, it is helpful to understand a proof of Kirchoff's theorem. Here is one outline.



          Theorem 1. (Binet-Cauchy) If $A$ is $m times n$ and $B$ is $n times m$ then
          $$ det(AB) = sum_S det(A[S]) det(B[S]) $$
          where the sum is over all $m$-element sets of columns of $A$ and rows of $B$. Here $A[S]$ means we keep only the columns from $S$ and $B[S]$ means we keep only the rows from $S$.



          Lemma 2. Let $G$ be a connected graph with no loops (but multiple edges are allowed). Arbitrarily orient $G$ and let $D$ be the signed incidence matrix:



          $$ D_{ve} = begin{cases} 1 & v text{ is the head of } e \
          -1 & v text{ is the tail of } e \
          0 & text{otherwise} end{cases} $$



          (Rows are indexed by vertices and columns by edges.) Then $DD^T = L$, the Laplacian of $G$.



          Lemma 3. Let $G$ be a connected graph with no loops, oriented arbitrarily. Let $D$ be the signed incidence matrix. If $v in V(G)$ and $S subseteq E(G)$ then $D(v,S)$ denotes the matrix with row $v$ deleted and keeping only the columns belonging to $S$. (So our sets $S$ have $|V(G)| - 1$ elements.)



          Then $det D(v,S) = pm 1$ if $(V,S)$ is a spanning tree and $det(v,S) = 0$ if $(V,S)$ is not (i.e. it contains a cycle).





          There are of course other proofs of Kirchoff's Theorem. What we can see here is that Lemma 3 is the tool that connects spanning trees to determinants and it is a result about the signed incidence matrix. From Lemma 2 and Binet-Cauchy, we remove the issue with having a sign of $pm 1$ by squaring and we sum over all sets $S$ to count the total number of trees.



          Now, if you are given the adjacency matrix, it is easy enough to construct the degrees of each vertex and hence construct the number of spanning trees. On the other hand, if you only have the degrees, you don't know enough about the graph structure to count spanning trees. As an exercise, find two graphs with the same degree sequence but different numbers of spanning trees (remember you can use multiple edges).






          share|cite|improve this answer









          $endgroup$



          It does not work with the adjancency matrix nor the degree matrix. To see this, let's look at $K_4$, which we know has $4^{4 - 2}$ spanning trees. (There's nothing special about $K_4$, it's just the first graph I picked.)



          $K_4$ has $4$ vertices each of degree $3$. Applying Kirchoff rule to the degree matrix gives $3^3$ spanning trees (incorrect). Applying it to the adjacency matrix gives
          $$ left| begin{array}{ccc} 0 & 1 & 1 \ 1 & 0 & 1 \ 1 & 1 & 0 end{array} right| = 2$$
          (also incorrect).



          To understand why you need the Laplacian, it is helpful to understand a proof of Kirchoff's theorem. Here is one outline.



          Theorem 1. (Binet-Cauchy) If $A$ is $m times n$ and $B$ is $n times m$ then
          $$ det(AB) = sum_S det(A[S]) det(B[S]) $$
          where the sum is over all $m$-element sets of columns of $A$ and rows of $B$. Here $A[S]$ means we keep only the columns from $S$ and $B[S]$ means we keep only the rows from $S$.



          Lemma 2. Let $G$ be a connected graph with no loops (but multiple edges are allowed). Arbitrarily orient $G$ and let $D$ be the signed incidence matrix:



          $$ D_{ve} = begin{cases} 1 & v text{ is the head of } e \
          -1 & v text{ is the tail of } e \
          0 & text{otherwise} end{cases} $$



          (Rows are indexed by vertices and columns by edges.) Then $DD^T = L$, the Laplacian of $G$.



          Lemma 3. Let $G$ be a connected graph with no loops, oriented arbitrarily. Let $D$ be the signed incidence matrix. If $v in V(G)$ and $S subseteq E(G)$ then $D(v,S)$ denotes the matrix with row $v$ deleted and keeping only the columns belonging to $S$. (So our sets $S$ have $|V(G)| - 1$ elements.)



          Then $det D(v,S) = pm 1$ if $(V,S)$ is a spanning tree and $det(v,S) = 0$ if $(V,S)$ is not (i.e. it contains a cycle).





          There are of course other proofs of Kirchoff's Theorem. What we can see here is that Lemma 3 is the tool that connects spanning trees to determinants and it is a result about the signed incidence matrix. From Lemma 2 and Binet-Cauchy, we remove the issue with having a sign of $pm 1$ by squaring and we sum over all sets $S$ to count the total number of trees.



          Now, if you are given the adjacency matrix, it is easy enough to construct the degrees of each vertex and hence construct the number of spanning trees. On the other hand, if you only have the degrees, you don't know enough about the graph structure to count spanning trees. As an exercise, find two graphs with the same degree sequence but different numbers of spanning trees (remember you can use multiple edges).







          share|cite|improve this answer












          share|cite|improve this answer



          share|cite|improve this answer










          answered Dec 7 '18 at 5:32









          Trevor GunnTrevor Gunn

          14.3k32046




          14.3k32046












          • $begingroup$
            First of all, thank you for your wonderful explanation! One question though, Are you suggesting that if I were given a large adjacency matrix, I should go ahead and try to map it out and then try to get the Laplacian matrix from that to get the spanning tree #?
            $endgroup$
            – DevAllanPer
            Dec 7 '18 at 5:46










          • $begingroup$
            Just for further reference, and so you can see what I mean, the matrix I was provided is: ibb.co/XXHL529 (i believe the missing # is 1)
            $endgroup$
            – DevAllanPer
            Dec 7 '18 at 5:48










          • $begingroup$
            @DevAllanPer By "map it out" do you mean draw it? That's unnecessary. All you need are the degrees and the adjacency matrix and you can read off the degrees from the adjacency matrix. For example, if vertex 1 is adjacent to vertices 5,7,10 then vertex 1 has degree 3.
            $endgroup$
            – Trevor Gunn
            Dec 7 '18 at 6:05










          • $begingroup$
            Yeah, but in my example (the link i provided) that creates a 8x8 matrix. I don't think our professor would be cruel enough to make us find the determinant of an 8x8 matrix, so whats the trick there?
            $endgroup$
            – DevAllanPer
            Dec 7 '18 at 15:59










          • $begingroup$
            @DevAllanPer Use a computer? I don't know.
            $endgroup$
            – Trevor Gunn
            Dec 7 '18 at 18:30


















          • $begingroup$
            First of all, thank you for your wonderful explanation! One question though, Are you suggesting that if I were given a large adjacency matrix, I should go ahead and try to map it out and then try to get the Laplacian matrix from that to get the spanning tree #?
            $endgroup$
            – DevAllanPer
            Dec 7 '18 at 5:46










          • $begingroup$
            Just for further reference, and so you can see what I mean, the matrix I was provided is: ibb.co/XXHL529 (i believe the missing # is 1)
            $endgroup$
            – DevAllanPer
            Dec 7 '18 at 5:48










          • $begingroup$
            @DevAllanPer By "map it out" do you mean draw it? That's unnecessary. All you need are the degrees and the adjacency matrix and you can read off the degrees from the adjacency matrix. For example, if vertex 1 is adjacent to vertices 5,7,10 then vertex 1 has degree 3.
            $endgroup$
            – Trevor Gunn
            Dec 7 '18 at 6:05










          • $begingroup$
            Yeah, but in my example (the link i provided) that creates a 8x8 matrix. I don't think our professor would be cruel enough to make us find the determinant of an 8x8 matrix, so whats the trick there?
            $endgroup$
            – DevAllanPer
            Dec 7 '18 at 15:59










          • $begingroup$
            @DevAllanPer Use a computer? I don't know.
            $endgroup$
            – Trevor Gunn
            Dec 7 '18 at 18:30
















          $begingroup$
          First of all, thank you for your wonderful explanation! One question though, Are you suggesting that if I were given a large adjacency matrix, I should go ahead and try to map it out and then try to get the Laplacian matrix from that to get the spanning tree #?
          $endgroup$
          – DevAllanPer
          Dec 7 '18 at 5:46




          $begingroup$
          First of all, thank you for your wonderful explanation! One question though, Are you suggesting that if I were given a large adjacency matrix, I should go ahead and try to map it out and then try to get the Laplacian matrix from that to get the spanning tree #?
          $endgroup$
          – DevAllanPer
          Dec 7 '18 at 5:46












          $begingroup$
          Just for further reference, and so you can see what I mean, the matrix I was provided is: ibb.co/XXHL529 (i believe the missing # is 1)
          $endgroup$
          – DevAllanPer
          Dec 7 '18 at 5:48




          $begingroup$
          Just for further reference, and so you can see what I mean, the matrix I was provided is: ibb.co/XXHL529 (i believe the missing # is 1)
          $endgroup$
          – DevAllanPer
          Dec 7 '18 at 5:48












          $begingroup$
          @DevAllanPer By "map it out" do you mean draw it? That's unnecessary. All you need are the degrees and the adjacency matrix and you can read off the degrees from the adjacency matrix. For example, if vertex 1 is adjacent to vertices 5,7,10 then vertex 1 has degree 3.
          $endgroup$
          – Trevor Gunn
          Dec 7 '18 at 6:05




          $begingroup$
          @DevAllanPer By "map it out" do you mean draw it? That's unnecessary. All you need are the degrees and the adjacency matrix and you can read off the degrees from the adjacency matrix. For example, if vertex 1 is adjacent to vertices 5,7,10 then vertex 1 has degree 3.
          $endgroup$
          – Trevor Gunn
          Dec 7 '18 at 6:05












          $begingroup$
          Yeah, but in my example (the link i provided) that creates a 8x8 matrix. I don't think our professor would be cruel enough to make us find the determinant of an 8x8 matrix, so whats the trick there?
          $endgroup$
          – DevAllanPer
          Dec 7 '18 at 15:59




          $begingroup$
          Yeah, but in my example (the link i provided) that creates a 8x8 matrix. I don't think our professor would be cruel enough to make us find the determinant of an 8x8 matrix, so whats the trick there?
          $endgroup$
          – DevAllanPer
          Dec 7 '18 at 15:59












          $begingroup$
          @DevAllanPer Use a computer? I don't know.
          $endgroup$
          – Trevor Gunn
          Dec 7 '18 at 18:30




          $begingroup$
          @DevAllanPer Use a computer? I don't know.
          $endgroup$
          – Trevor Gunn
          Dec 7 '18 at 18:30


















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